Prof. Ameya Jagtap

I am an Assistant Professor (tenure-track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to joining WPI, I served as an Assistant Professor of Applied Mathematics (Research) at Brown University for three and a half years. My academic journey includes earning both my PhD and Master's degrees in Aerospace Engineering from the esteemed Indian Institute of Science (IISc) in India. Following this, I engaged in postdoctoral research at the Tata Institute of Fundamental Research—Center for Applicable Mathematics (TIFR-CAM) in India. Subsequently, I transitioned to Brown University to continue my postdoctoral research within the Division of Applied Mathematics.

My research is uniquely positioned at the intersection of mechanical/aerospace engineering, applied mathematics, and computation. I am particularly dedicated to advancing scientific machine learning algorithms that seamlessly integrate data and physics, offering versatile applications across computational physics. My areas of expertise encompass scientific machine learning, deep learning, data- and physics-driven deep learning techniques, uncertainty quantification, and propagation, as well as multi-scale/multi-physics simulations (solids, fluids, and acoustics). I bring proficiency in spectral/finite element methods, WENO/DG schemes, and domain decomposition techniques, among others. Beyond these, I am actively engaged in more traditional machine learning algorithms such as deep generative models, and novel artificial neural network architectures, such as quantum and graph neural networks. To this end, my interests also extend to spiking neural networks and other bio-inspired computing techniques. 

TBD

Seminar date and time: TBD.

Zoom information here.

You?

Please reach out to rmaulik@purdue.edu if you are interested in presenting in the ISCL Seminar Series! Graduate students and postdocs are particularly encouraged to present their work.

Seminar date and time: TBA. 10 AM ET.

Zoom link here.